Fastbook notebook(3 & 4)

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Basics of deep learning

Lecture 3 is about data ethics, It's self Explanatory and Rachel’s Lecture is more than sufficient. 

Lecture 4 is more about breaking the myth behind neural networks. And the basics of pytorch and how fastai api makes deep learning cool.

You may come across jargon like Sigmoid, Relu, Stochastic Gradient Descent, Learning rate and so on. Their explanation is great. They explained maths with a piece of code and I completely enjoyed it.

What i would suggest is play with notebook 4 and learn more things. In case you don't understand something, go through it again and again and you will catch up. If you want some help ask the question in the forum.

You can practise lecture 4 by downloading the repository and going to the clean folder, there is only code not with a prose. It's the best place to practise what you have learned till now.

The motto is, If you are in doubt, run the code.

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